Development of a vehicle body velocity sensor using Modulated Motion Blur

Author(s):  
Minyoung Lee ◽  
Kyung-Soo Kim ◽  
Jungseok Cho ◽  
Soohyun Kim
2001 ◽  
Vol 29 (1) ◽  
pp. 2-22 ◽  
Author(s):  
T. Okano ◽  
M. Koishi

Abstract “Hydroplaning characteristics” is one of the key functions for safe driving on wet roads. Since hydroplaning depends on vehicle velocity as well as the tire construction and tread pattern, a predictive simulation tool, which reflects all these effects, is required for effective and precise tire development. A numerical analysis procedure predicting the onset of hydroplaning of a tire, including the effect of vehicle velocity, is proposed in this paper. A commercial explicit-type FEM (finite element method)/FVM (finite volume method) package is used to solve the coupled problems of tire deformation and flow of the surrounding fluid. Tire deformations and fluid flows are solved, using FEM and FVM, respectively. To simulate transient phenomena effectively, vehicle-body-fixed reference-frame is used in the analysis. The proposed analysis can accommodate 1) complex geometry of the tread pattern and 2) rotational effect of tires, which are both important functions of hydroplaning simulation, and also 3) velocity dependency. In the present study, water is assumed to be compressible and also a laminar flow, indeed the fluid viscosity, is not included. To verify the effectiveness of the method, predicted hydroplaning velocities for four different simplified tread patterns are compared with experimental results measured at the proving ground. It is concluded that the proposed numerical method is effective for hydroplaning simulation. Numerical examples are also presented in which the present simulation methods are applied to newly developed prototype tires.


Author(s):  
Marco Di Clemente ◽  
Marco Marini ◽  
Sara Di Benedetto ◽  
Antonio Schettino ◽  
Giuliano Ranuzzi

2019 ◽  
Vol 12 (4) ◽  
pp. 357-366
Author(s):  
Yong Song ◽  
Shichuang Liu ◽  
Jiangxuan Che ◽  
Jinyi Lian ◽  
Zhanlong Li ◽  
...  

Background: Vehicles generally travel on different road conditions, and withstand strong shock and vibration. In order to reduce or isolate the strong shock and vibration, it is necessary to propose and develop a high-performance vehicle suspension system. Objective: This study aims to report a pneumatic artificial muscle bionic kangaroo leg suspension to improve the comfort performance of vehicle suspension system. Methods: In summarizing the existing vehicle suspension systems and analyzing their advantages and disadvantages, this paper introduces a new patent of vehicle suspension system based on the excellent damping and buffering performance of kangaroo leg, A Pneumatic Artificial Muscle Bionic Kangaroo Leg Suspension. According to the biomimetic principle, the pneumatic artificial muscles bionic kangaroo leg suspension with equal bone ratio is constructed on the basis of the kangaroo leg crural index, and two working modes (passive and active modes) are designed for the suspension. Moreover, the working principle of the suspension system is introduced, and the rod system equations for the suspension structure are built up. The characteristic simulation model of this bionic suspension is established in Adams, and the vertical performance is analysed. Results: It is found that the largest deformation happens in the bionic heel spring and the largest angle change occurs in the bionic ankle joint under impulse road excitation, which is similar to the dynamic characteristics of kangaroo leg. Furthermore, the dynamic displacement and the acceleration of the vehicle body are both sharply reduced. Conclusion: The simulation results show that the comfort performance of this bionic suspension is excellent under the impulse road excitation, which indicates the bionic suspension structure is feasible and reasonable to be applied to vehicle suspensions.


2021 ◽  
pp. 107754632199759
Author(s):  
Jianchun Yao ◽  
Mohammad Fard ◽  
John L Davy ◽  
Kazuhito Kato

Industry is moving towards more data-oriented design and analyses to solve complex analytical problems. Solving complex and large finite element models is still challenging and requires high computational time and resources. Here, a modular method is presented to predict the transmission of vehicle body vibration to the occupants’ body by combining the numerical transfer matrices of the subsystems. The transfer matrices of the subsystems are presented in the form of data which is sourced from either physical tests or finite element models. The structural dynamics of the vehicle body is represented using a transfer matrix at each of the seat mounting points in three triaxial (X–Y–Z) orientations. The proposed method provides an accurate estimation of the transmission of the vehicle body vibration to the seat frame and the seated occupant. This method allows the combination of conventional finite element analytical model data and the experimental data of subsystems to accurately predict the dynamic performance of the complex structure. The numerical transfer matrices can also be the subject of machine learning for various applications such as for the prediction of the vibration discomfort of the occupant with different seat and foam designs and with different physical characteristics of the occupant body.


Author(s):  
Li Zhao ◽  
Laurence Rilett ◽  
Mm Shakiul Haque

This paper develops a methodology for simultaneously modeling lane-changing and car-following behavior of automated vehicles on freeways. Naturalistic driving data from the Safety Pilot Model Deployment (SPMD) program are used. First, a framework to process the SPMD data is proposed using various data analytics techniques including data fusion, data mining, and machine learning. Second, pairs of automated host vehicle and their corresponding front vehicle are identified along with their lane-change and car-following relationship data. Using these data, a lane-changing-based car-following (LCCF) model, which explicitly considers lane-change and car-following behavior simultaneously, is developed. The LCCF model is based on Gaussian-mixture-based hidden Markov model theory and is disaggregated into two processes: LCCF association and LCCF dissociation. These categories are based on the result of the lane change. The overall goal is to predict a driver’s lane-change intention using the LCCF model. Results show that the model can predict the lane-change event in the order of 0.6 to 1.3 s before the moment of the vehicle body across the lane boundary. In addition, the execution times of lane-change maneuvers average between 0.55 and 0.86 s. The LCCF model allows the intention time and execution time of driver’s lane-change behavior to be forecast, which will help to develop better advanced driver assistance systems for vehicle controls with respect to lane-change and car-following warning functions.


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